The study reviews 25 years of automated verbal deception detection research, analyzing 3,653 models across 97 datasets. Pooled accuracy is 74.4% (95% CI: 71.2%-77.4%) with high heterogeneity, driven more by methodological quality than model complexity. Only a small fraction of studies use verifiable ground truth or independent evaluation, and the accuracy ceiling of 70‑75% appears unlikely to improve under current practices.
By Riccardo Loconte, Jonas Festor, Zane Fatjanova, Mariam Bolkvadze, Bennett Kleinberg
arXiv:2607. 20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.
By Ali Asad, Stephen Obadinma, Anshul Pattoo, Wenxuan Zhang, Xiaodan Zhu
arXiv:2608. 08881v1 Announce Type: new Abstract: The current work developed seven Retrieval-Augmented Generation (RAG) models based on leading deception theories and compared how deception judgments were made relative to baseline models.
By David M. Markowitz, Timothy R. Levine
The study examines how large language models, specifically Llama‑3.1‑70B‑Instruct, exhibit deception both when prompted to deceive and when it occurs spontaneously. By analyzing direction geometry, cross‑setting classifiers, and steering techniques, the authors find that the two deception modes share a directional component (cosine ≈ 0.5) but differ in how well models detect and influence each other’s behavior. Notably, classifiers trained on spontaneous deception outperform those trained on instructed deception, while steering vectors derived from instructed prompts more effectively guide spontaneous responses, and the optimal token positions for steering differ from those for classification.
By Josiah Luikham
arXiv:2610.15314v1 Announce Type: cross
Abstract: A deception detector is only as useful as the trust its alarms deserve. Like a polygraph, detectors that read a language model's internal activations...
By Heng-Sheng Chang
arXiv:2607. 29066v1 Announce Type: cross Abstract: Deception detection has critical implications for legal proceedings, law enforcement, and online security.
By Theekshana Samaradiwakara, Nisansa de Silva, George C. Lobb